Optimization-Based Evolutionary Polynomial Regression

  • Zhen-Yu Yin
  • Yin-Fu Jin


This chapter aims to propose a robust and effective evolutionary polynomial regression (EPR) model for Cα of clay. First, a database covering various clays is formed, in which 120 data are randomly selected for training and the remaining data are used for testing. To avoid overfitting, a novel EPR procedure using a newly enhanced differential evolution (DE) algorithm is proposed with two enhancements: (1) a new fitness function is proposed using the structural risk minimization (SRM) with L2 regularization that penalizes polynomial complexity, and (2) an adaptive process for selecting the combination of involved variables and size of polynomial terms is incorporated. By comparing the predictive ability, model complexity, robustness and monotonicity, the EPR formulation for Cα involving clay content, plasticity index and void ratio with three terms is selected as the optimal model. A parametric study is then conducted to assess the importance of each input in the proposed model. All results demonstrate that the proposed model of Cα is simple, robust, and reliable for applications in engineering practice.


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© Springer Nature Singapore Pte Ltd. and Tongji University Press 2019

Authors and Affiliations

  • Zhen-Yu Yin
    • 1
  • Yin-Fu Jin
    • 1
  1. 1.Department of Civil and Environmental EngineeringHong Kong Polytechnic UniversityHong KongChina

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